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Advanced Satellite Image Processing for Agricultural and Environmental Monitoring
This special issue belongs to the section “Environmental Sciences“.
Special Issue Information
Dear Colleagues,
Satellite remote sensing has become an essential tool for monitoring agricultural systems and environmental dynamics at multiple spatial and temporal scales. With the increasing availability of high-resolution, multispectral, hyperspectral, and radar satellite imagery, researchers and practitioners can now access unprecedented volumes of data to observe crop development, land use changes, soil moisture variability, deforestation, droughts, and other climate-related phenomena.
This Special Issue aims to explore recent advances in satellite image processing techniques applied to agricultural and environmental monitoring. It seeks to gather cutting-edge contributions that employ modern computational approaches such as machine learning, deep learning, and data fusion, as well as physics-based modelling and multi-temporal change detection. Special emphasis will be placed on studies that demonstrate practical applications related to sustainable agriculture, food security, climate change adaptation, biodiversity monitoring, and natural resource management.
We welcome original research articles, technical developments, and comprehensive reviews that focus on, but are not limited to, the following topics:
- Crop classification, growth monitoring, and yield prediction using satellite imagery;
- Detection and assessment of extreme weather events and their impacts on agriculture;
- Integration of satellite data with in situ or UAV observations;
- Time-series analysis for land cover and land use change detection;
- AI-enhanced feature extraction and segmentation in remote sensing;
- Soil health and vegetation indices derived from multi-sensor satellite data;
- Climate-smart agriculture through Earth observation technologies.
This Special Issue provides a platform for researchers, professionals, and policymakers to share innovative methods and applications that enhance the use of satellite-based Earth observation for monitoring agricultural systems and environmental sustainability in the face of a changing climate.
Dr. Nathalie dos Santos Guimarães
Dr. Helder José Chaves Fraga
Guest Editors
Manuscript Submission Information
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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- remote sensing
- machine learning
- deep learning
- crop monitoring
- image processing
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